ai-content-maker/.venv/Lib/site-packages/scipy/spatial/tests/test__plotutils.py

92 lines
3.7 KiB
Python
Raw Normal View History

2024-05-03 04:18:51 +03:00
import pytest
import numpy as np
from numpy.testing import assert_, assert_array_equal, assert_allclose
try:
import matplotlib
matplotlib.rcParams['backend'] = 'Agg'
import matplotlib.pyplot as plt
has_matplotlib = True
except Exception:
has_matplotlib = False
from scipy.spatial import \
delaunay_plot_2d, voronoi_plot_2d, convex_hull_plot_2d, \
Delaunay, Voronoi, ConvexHull
@pytest.mark.skipif(not has_matplotlib, reason="Matplotlib not available")
class TestPlotting:
points = [(0,0), (0,1), (1,0), (1,1)]
def test_delaunay(self):
# Smoke test
fig = plt.figure()
obj = Delaunay(self.points)
s_before = obj.simplices.copy()
r = delaunay_plot_2d(obj, ax=fig.gca())
assert_array_equal(obj.simplices, s_before) # shouldn't modify
assert_(r is fig)
delaunay_plot_2d(obj, ax=fig.gca())
def test_voronoi(self):
# Smoke test
fig = plt.figure()
obj = Voronoi(self.points)
r = voronoi_plot_2d(obj, ax=fig.gca())
assert_(r is fig)
voronoi_plot_2d(obj)
voronoi_plot_2d(obj, show_vertices=False)
def test_convex_hull(self):
# Smoke test
fig = plt.figure()
tri = ConvexHull(self.points)
r = convex_hull_plot_2d(tri, ax=fig.gca())
assert_(r is fig)
convex_hull_plot_2d(tri)
def test_gh_19653(self):
# aspect ratio sensitivity of voronoi_plot_2d
# infinite Voronoi edges
points = np.array([[245.059986986012, 10.971011721360075],
[320.49044143557785, 10.970258360366753],
[239.79023081978914, 13.108487516946218],
[263.38325791238833, 12.93241352743668],
[219.53334398353175, 13.346107628161008]])
vor = Voronoi(points)
fig = voronoi_plot_2d(vor)
ax = fig.gca()
infinite_segments = ax.collections[1].get_segments()
expected_segments = np.array([[[282.77256, -254.76904],
[282.729714, -4544.744698]],
[[282.77256014, -254.76904029],
[430.08561382, 4032.67658742]],
[[229.26733285, -20.39957514],
[-168.17167404, -4291.92545966]],
[[289.93433364, 5151.40412217],
[330.40553385, 9441.18887532]]])
assert_allclose(infinite_segments, expected_segments)
def test_gh_19653_smaller_aspect(self):
# reasonable behavior for less extreme aspect
# ratio
points = np.array([[24.059986986012, 10.971011721360075],
[32.49044143557785, 10.970258360366753],
[23.79023081978914, 13.108487516946218],
[26.38325791238833, 12.93241352743668],
[21.53334398353175, 13.346107628161008]])
vor = Voronoi(points)
fig = voronoi_plot_2d(vor)
ax = fig.gca()
infinite_segments = ax.collections[1].get_segments()
expected_segments = np.array([[[28.274979, 8.335027],
[28.270463, -42.19763338]],
[[28.27497869, 8.33502697],
[43.73223829, 56.44555501]],
[[22.51805823, 11.8621754],
[-12.09266506, -24.95694485]],
[[29.53092448, 78.46952378],
[33.82572726, 128.81934455]]])
assert_allclose(infinite_segments, expected_segments)